Platelet reactivity in response to aspirin and ticagrelor in African-Americans and European-Americans.

Platelet gene polymorphisms are associated with variable on-treatment platelet reactivity and vary by race. Whether differences in platelet reactivity and aspirin or ticagrelor exist between African-American and European-Americans remains poorly understood. Biological samples from three prior prospective antiplatelet challenge studies at the Duke Clinical Research Unit were used to compare platelet reactivity between African-American and European-American subjects. Platelet reactivity at baseline, on-aspirin, on-ticagrelor, and the treatment effect of aspirin or ticagrelor were compared between groups using an adjusted mixed effects model. Compared with European-Americans (n = 282; 50% female; mean ± standard deviation age, 50 ± 16), African-Americans (n = 209; 67% female; age 48 ± 12) had lower baseline platelet reactivity with platelet function analyzer-100 (PFA-100) (p < 0.01) and with light transmission aggregometry (LTA) in response to arachidonic acid (AA), adenosine diphosphate (ADP), and epinephrine agonists (p < 0.05). African-Americans had lower platelet reactivity on aspirin in response to ADP, epinephrine, and collagen (p < 0.05) and on ticagrelor in response to AA, ADP, and collagen (p < 0.05). The treatment effect of aspirin was greater in European-Americans with an AA agonist (p = 0.002). Between-race differences with in vitro aspirin mirrored those seen in vivo. The treatment effect of ticagrelor was greater in European-Americans in response to ADP (p < 0.05) but with collagen, the treatment effect was greater for African-Americans (p < 0.05). Platelet reactivity was overall lower in African-Americans off-treatment, on aspirin, and on ticagrelor. European-Americans experienced greater platelet suppression on aspirin and on ticagrelor. The aspirin response difference in vivo and in vitro suggests a mechanism intrinsic to the platelet. Whether the absolute level of platelet reactivity or the degree of platelet suppression after treatment is more important for clinical outcomes is uncertain.

Distinguishing heart failure with reduced ejection fraction from heart failure with preserved ejection fraction: A phenomics approach.

AIM: Pathophysiological differences between patients with heart failure with preserved (HFpEF) and reduced (HFrEF) ejection fraction (EF) remain unclear. Therefore we used a phenomics approach, integrating selected proteomics data with patient characteristics and cardiac structural and functional parameters, to get insight into differential pathophysiological mechanisms and identify potential treatment targets. METHODS AND RESULTS: We report data from a representative subcohort of the prospective Singapore Heart Failure Outcomes and Phenotypes (SHOP), including patients with HFrEF (EF <40%, n = 217), HFpEF (EF ≥50%, n = 213), and age- and sex-matched controls without HF (n = 216). We measured 92 biomarkers using a proximity extension assay and assessed cardiac structure and function in all participants using echocardiography. We used multi-block projection to latent structure analysis to integrate clinical, echocardiographic, and biomarker variables. Candidate biomarker targets were cross-referenced with small-molecule and drug databases. The total cohort had a median age of 65 years (interquartile range 60-71), and 50% were women. Protein profiles strongly discriminated patients with HFrEF (area under the curve [AUC] = 0.89) and HFpEF (AUC = 0.94) from controls. Phenomics analyses identified unique druggable inflammatory markers in HFpEF from the tumour necrosis factor receptor superfamily (TNFRSF), which were positively associated with hypertension, diabetes, and increased posterior and relative wall thickness. In HFrEF, interleukin (IL)-8 and IL-6 were possible targets related to lower EF and worsening renal function. CONCLUSION: We identified pathophysiological mechanisms related to increased cardiac wall thickness parameters and potentially druggable inflammatory markers from the TNFRSF in HFpEF.

Automated detection of coronary artery disease, myocardial infarction and congestive heart failure using GaborCNN model with ECG signals.

Cardiovascular diseases (CVDs) are main causes of death globally with coronary artery disease (CAD) being the most important. Timely diagnosis and treatment of CAD is crucial to reduce the incidence of CAD complications like myocardial infarction (MI) and ischemia-induced congestive heart failure (CHF). Electrocardiogram (ECG) signals are most commonly employed as the diagnostic screening tool to detect CAD. In this study, an automated system (AS) was developed for the automated categorization of electrocardiogram signals into normal, CAD, myocardial infarction (MI) and congestive heart failure (CHF) classes using convolutional neural network (CNN) and unique GaborCNN models. Weight balancing was used to balance the imbalanced dataset. High classification accuracies of more than 98.5% were obtained by the CNN and GaborCNN models respectively, for the 4-class classification of normal, coronary artery disease, myocardial infarction and congestive heart failure classes. GaborCNN is a more preferred model due to its good performance and reduced computational complexity as compared to the CNN model. To the best of our knowledge, this is the first study to propose GaborCNN model for automated categorizing of normal, coronary artery disease, myocardial infarction and congestive heart failure classes using ECG signals. Our proposed system is equipped to be validated with bigger database and has the potential to aid the clinicians to screen for CVDs using ECG signals.

Polygenic Risk Scores for Atherosclerotic Cardiovascular Disease in the Asia-Pacific Region.

Approximately one-half of the phenotypic susceptibility to atherosclerotic cardiovascular disease (ASCVD) has a genetic basis. Although individual allelic variants generally impart a small effect on risk for ASCVD, an emerging body of data has shown that the aggregation and weighting of many of these genetic variations into "scores" can further discriminate an individual's risk beyond traditional risk factors alone. Consistent with the theory of population genetics, such polygenic risk scores (PRS) appear to be ethnicity specific because their elements comprise single-nucleotide variants that are always ethnicity specific. The currently available PRS are derived predominantly from European ancestry and thus predictably perform less well among non-European participants, a fact that has implications for their use in the Asia-Pacific region. This paper describes the current state of knowledge of PRS, the available data that support their use in this region, and highlights the needs moving forward to safely and effectively implement them in clinical care in the Asia-Pacific region.

Machine learning versus classical electrocardiographic criteria for echocardiographic left ventricular hypertrophy in a pre-participation cohort.

BACKGROUND: Classical electrocardiographic (ECG) criteria for left ventricular hypertrophy (LVH) are well studied in older populations and patients with hypertension. Their utility in young pre-participation cohorts is unclear. AIMS: We aimed to develop machine learning models for detection of echocardiogram-diagnosed LVH from ECG, and compare these models with classical criteria. METHODS: Between November 2009 and December 2014, pre-participation screening ECG and subsequent echocardiographic data was collected from 17 310 males aged 16 to 23, who reported for medical screening prior to military conscription. A final diagnosis of LVH was made during echocardiography, defined by a left ventricular mass index >115 g/m2. The continuous and threshold forms of classical ECG criteria (Sokolow-Lyon, Romhilt-Estes, Modified Cornell, Cornell Product, and Cornell) were compared against machine learning models (Logistic Regression, GLMNet, Random Forests, Gradient Boosting Machines) using receiver-operating characteristics curve analysis. We also compared the important variables identified by machine learning models with the input variables of classical criteria. RESULTS: Prevalence of echocardiographic LVH in this population was 0.82% (143/17310). Classical ECG criteria had poor performance in predicting LVH. Machine learning methods achieved superior performance: Logistic Regression (area under the curve [AUC], 0.811; 95% confidence interval [CI], 0.738-0.884), GLMNet (AUC, 0.873; 95% CI, 0.817-0.929), Random Forest (AUC, 0.824; 95% CI, 0.749-0.898), Gradient Boosting Machines (AUC, 0.800; 95% CI, 0.738-0.862). CONCLUSIONS: Machine learning methods are superior to classical ECG criteria in diagnosing echocardiographic LVH in the context of pre-participation screening.

A computational intelligence tool for the detection of hypertension using empirical mode decomposition.

Hypertension (HPT), also known as high blood pressure, is a precursor to heart, brain or kidney diseases. Some symptoms of HPT include headaches, dizziness and fainting. The potential diagnosis of masked hypertension is of specific interest in this study. In masked hypertension (MHPT), the instantaneous blood pressure appears normal, but the 24-h ambulatory blood pressure is abnormal. Hence patients with MHPT are difficult to identify and thus remain untreated or are treated insufficiently. Hence, a computational intelligence tool (CIT) using electrocardiograms (ECG) signals for HPT and possible MHPT detection is proposed in this work. Empirical mode decomposition (EMD) is employed to decompose the pre-processed signals up to five levels. Nonlinear features are extracted from the five intrinsic mode functions (IMFs) thereafter. Student's t-test is subsequently applied to select a set of highly discriminatory features. This feature set is then input to various classifiers, in which, the best accuracy of 97.70% is yielded by the k-nearest neighbor (k-NN) classifier. The developed tool is evaluated by the 10-fold cross validation technique. Our findings suggest that the developed system is useful for diagnostic computational intelligence tool in hospital settings, and that it enables the automatic classification of HPT versus normal ECG signals.

Automated pre-screening of arrhythmia using hybrid combination of Fourier-Bessel expansion and LSTM.

Health care in developing countries demands systems-based screening solutions. In view of this, we present a new rhythm-based methodology for the point-of-care diagnosis of cardiac arrhythmia at a primary level. Such a system will reduce the workload of cardiologists significantly. The method begins by computing the RR-interval sequences from the electrocardiogram(ECG) signals. Then, the Fourier-Bessel (FB) expansion is used to obtain the intelligent series by converting the RR-interval sequences into more meaningful sequences that can characterize the underlying pathology of cardiac arrhythmia with a unique pattern. Ultimately, the obtained intelligent series are used as input to train the long short-term memory (LSTM) model for ECG classification. We have obtained an accuracy of 90.07% in classifying normal and the arrhythmia classes using MIT-BIH database. The results demonstrate that the proposed intelligent series can reveal remarkable differences between the normal and arrhythmia ECG signals. Thus, the proposed algorithm can be used as a primary screening tool for detecting cardiac arrhythmia. Potentially, the developed system can be used by paramedics in rural outreach programs with limited funding and expertise. Moreover, the use of single-lead and short-length ECG signals in the proposed system makes it a suitable candidate for applications that are intended for mobile and other hand-held or wearable devices.

Comprehensive electrocardiographic diagnosis based on deep learning.

Cardiovascular disease (CVD) is the leading cause of death worldwide, and coronary artery disease (CAD) is a major contributor. Early-stage CAD can progress if undiagnosed and left untreated, leading to myocardial infarction (MI) that may induce irreversible heart muscle damage, resulting in heart chamber remodeling and eventual congestive heart failure (CHF). Electrocardiography (ECG) signals can be useful to detect established MI, and may also be helpful for early diagnosis of CAD. For the latter especially, the ECG perturbations can be subtle and potentially misclassified during manual interpretation and/or when analyzed by traditional algorithms found in ECG instrumentation. For automated diagnostic systems (ADS), deep learning techniques are favored over conventional machine learning techniques, due to the automatic feature extraction and selection processes involved. This paper highlights various deep learning algorithms exploited for the classification of ECG signals into CAD, MI, and CHF conditions. The Convolutional Neural Network (CNN), followed by combined CNN and Long Short-Term Memory (LSTM) models, appear to be the most useful architectures for classification. A 16-layer LSTM model was developed in our study and validated using 10-fold cross-validation. A classification accuracy of 98.5% was achieved. Our proposed model has the potential to be a useful diagnostic tool in hospitals for the classification of abnormal ECG signals.

Gadobutrol-Enhanced Cardiac Magnetic Resonance Imaging for Detection of Coronary Artery Disease.

BACKGROUND: Gadolinium-based contrast agents were not approved in the United States for detecting coronary artery disease (CAD) prior to the current studies. OBJECTIVES: The purpose of this study was to determine the sensitivity and specificity of gadobutrol for detection of CAD by assessing myocardial perfusion and late gadolinium enhancement (LGE) imaging. METHODS: Two international, single-vendor, phase 3 clinical trials of near identical design, "GadaCAD1" and "GadaCAD2," were performed. Cardiovascular magnetic resonance (CMR) included gadobutrol-enhanced first-pass vasodilator stress and rest perfusion followed by LGE imaging. CAD was defined by quantitative coronary angiography (QCA) but computed tomography coronary angiography could exclude significant CAD. RESULTS: Because the design and results for GadaCAD1 (n = 376) and GadaCAD2 (n = 388) were very similar, results were summarized as a fixed-effect meta-analysis (n = 764). The prevalence of CAD was 27.8% defined by a ≥70% QCA stenosis. For detection of a ≥70% QCA stenosis, the sensitivity of CMR was 78.9%, specificity was 86.8%, and area under the curve was 0.871. The sensitivity and specificity for multivessel CAD was 87.4% and 73.0%. For detection of a 50% QCA stenosis, sensitivity was 64.6% and specificity was 86.6%. The optimal threshold for detecting CAD was a ≥67% QCA stenosis in GadaCAD1 and ≥63% QCA stenosis in GadaCAD2. CONCLUSIONS: Vasodilator stress and rest myocardial perfusion CMR and LGE imaging had high diagnostic accuracy for CAD in 2 phase 3 clinical trials. These findings supported the U.S. Food and Drug Administration approval of gadobutrol-enhanced CMR (0.1 mmol/kg) to assess myocardial perfusion and LGE in adult patients with known or suspected CAD.

Computer-aided diagnosis of congestive heart failure using ECG signals - A review.

The heart muscle pumps blood to vital organs, which is indispensable for human life. Congestive heart failure (CHF) is characterized by the inability of the heart to pump blood adequately throughout the body without an increase in intracardiac pressure. The symptoms include lung and peripheral congestion, leading to breathing difficulty and swollen limbs, dizziness from reduced delivery of blood to the brain, as well as arrhythmia. Coronary artery disease, myocardial infarction, and medical co-morbidities such as kidney disease, diabetes, and high blood pressure all take a toll on the heart and can impair myocardial function. CHF prevalence is growing worldwide. It afflicts millions of people globally, and is a leading cause of death. Hence, proper diagnosis, monitoring and management are imperative. The importance of an objective CHF diagnostic tool cannot be overemphasized. Standard diagnostic tests for CHF include chest X-ray, magnetic resonance imaging (MRI), nuclear imaging, echocardiography, and invasive angiography. However, these methods are costly, time-consuming, and they can be operator-dependent. Electrocardiography (ECG) is inexpensive and widely accessible, but ECG changes are typically not specific for CHF diagnosis. A properly designed computer-aided detection (CAD) system for CHF, based on the ECG, would potentially reduce subjectivity and provide quantitative assessment for informed decision-making. Herein, we review existing CAD for automatic CHF diagnosis, and highlight the development of an ECG-based CAD diagnostic system that employs deep learning algorithms to automatically detect CHF.

Automated detection of shockable and non-shockable arrhythmia using novel wavelet-based ECG features.

Malignant arrhythmia can lead to sudden cardiac death (SCD). Shockable arrhythmia can be terminated with device electrical shock therapies. Ventricular-tachycardia (VT) and ventricular fibrillation (VF) are responsive to electrical anti-tachycardia pacing therapy and defibrillation which help to restore normal electrical and mechanical function of the heart. In contrast, non-shockable arrhythmia like asystole and bradycardia are not responsive to electric shock therapy. Distinguishing between shockable and non-shockable arrhythmia is an important diagnostic challenge that has practical clinical relevance. It is difficult to accurately differentiate between these two types of arrhythmia by manual inspection of electrocardiogram (ECG) segments within the short time duration before triggering the device for electrical therapy. Automated defibrillators are equipped with automatic shockable arrhythmia detection algorithms based on ECG morphological features, which may possess variable diagnostic performance depending on machine models. In our work, we have designed a robust system using wavelet decomposition filter banks for extraction of features from the ECG signal and then classifying the features. We believe this method will improve the accuracy of discriminating between shockable and non-shockable arrhythmia compared with existing conventional algorithms. We used a novel three channel orthogonal wavelet filter bank, which extracted features from ECG epochs of duration 2 s to distinguish between shockable and non-shockable arrhythmia. The fuzzy, Renyi and sample entropies are extracted from the various wavelet coefficients and fed to support vector machine (SVM) classifier for automated classification. We have obtained an accuracy of 98.9%, sensitivity and specificity of 99.08% and 97.11.9%, respectively, using 10-fold cross validation. The area under the receiver operating characteristic has been found to be 0.99 with F1-score of 0.994. The system developed is more accurate than the existing algorithms. Hence, the proposed system can be employed in automated defibrillators inside and outside hospitals for emergency revival of patients suffering from SCD. These automated defibrillators can also be implanted inside the human body for automatic detection of potentially fatal shockable arrhythmia and to deliver an appropriate electric shock to the heart.

Automated detection of atrial fibrillation using long short-term memory network with RR interval signals.

Atrial Fibrillation (AF), either permanent or intermittent (paroxysnal AF), increases the risk of cardioembolic stroke. Accurate diagnosis of AF is obligatory for initiation of effective treatment to prevent stroke. Long term cardiac monitoring improves the likelihood of diagnosing paroxysmal AF. We used a deep learning system to detect AF beats in Heart Rate (HR) signals. The data was partitioned with a sliding window of 100 beats. The resulting signal blocks were directly fed into a deep Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM). The system was validated and tested with data from the MIT-BIH Atrial Fibrillation Database. It achieved 98.51% accuracy with 10-fold cross-validation (20 subjects) and 99.77% with blindfold validation (3 subjects). The proposed system structure is straight forward, because there is no need for information reduction through feature extraction. All the complexity resides in the deep learning system, which gets the entire information from a signal block. This setup leads to the robust performance for unknown data, as measured with the blind fold validation. The proposed Computer-Aided Diagnosis (CAD) system can be used for long-term monitoring of the human heart. To the best of our knowledge, the proposed system is the first to incorporate deep learning for AF beat detection.

Importance of angina in patients with coronary disease, heart failure, and left ventricular systolic dysfunction: insights from STICH.

BACKGROUND: Patients with left ventricular (LV) systolic dysfunction, coronary artery disease (CAD), and angina are often thought to have a worse prognosis and a greater prognostic benefit from coronary artery bypass graft (CABG) surgery than those without angina. OBJECTIVES: This study investigated: 1) whether angina was associated with a worse prognosis; 2) whether angina identified patients who had a greater survival benefit from CABG; and 3) whether CABG improved angina in patients with LV systolic dysfunction and CAD. METHODS: We performed an analysis of the STICH (Surgical Treatment for Ischemic Heart Failure) trial, in which 1,212 patients with an ejection fraction ≤35% and CAD were randomized to CABG or medical therapy. Multivariable Cox and logistic models were used to assess long-term clinical outcomes. RESULTS: At baseline, 770 patients (64%) reported angina. Among patients assigned to medical therapy, all-cause mortality was similar in patients with and without angina (hazard ratio [HR]: 1.05; 95% confidence interval [CI]: 0.79 to 1.38). The effect of CABG was similar whether the patient had angina (HR: 0.89; 95% CI: 0.71 to 1.13) or not (HR: 0.68; 95% CI: 0.50 to 0.94; p interaction = 0.14). Patients assigned to CABG were more likely to report improvement in angina than those assigned to medical therapy alone (odds ratio: 0.70; 95% CI: 0.55 to 0.90; p < 0.01). CONCLUSIONS: Angina does not predict all-cause mortality in medically treated patients with LV systolic dysfunction and CAD, nor does it identify patients who have a greater survival benefit from CABG. However, CABG does improve angina to a greater extent than medical therapy alone. (Comparison of Surgical and Medical Treatment for Congestive Heart Failure and Coronary Artery Disease [STICH]; NCT00023595).

Integrated allelic, transcriptional, and phenomic dissection of the cardiac effects of titin truncations in health and disease.

The recent discovery of heterozygous human mutations that truncate full-length titin (TTN, an abundant structural, sensory, and signaling filament in muscle) as a common cause of end-stage dilated cardiomyopathy (DCM) promises new prospects for improving heart failure management. However, realization of this opportunity has been hindered by the burden of TTN-truncating variants (TTNtv) in the general population and uncertainty about their consequences in health or disease. To elucidate the effects of TTNtv, we coupled TTN gene sequencing with cardiac phenotyping in 5267 individuals across the spectrum of cardiac physiology and integrated these data with RNA and protein analyses of human heart tissues. We report diversity of TTN isoform expression in the heart, define the relative inclusion of TTN exons in different isoforms (using the TTN transcript annotations available at http://cardiodb.org/titin), and demonstrate that these data, coupled with the position of the TTNtv, provide a robust strategy to discriminate pathogenic from benign TTNtv. We show that TTNtv is the most common genetic cause of DCM in ambulant patients in the community, identify clinically important manifestations of TTNtv-positive DCM, and define the penetrance and outcomes of TTNtv in the general population. By integrating genetic, transcriptome, and protein analyses, we provide evidence for a length-dependent mechanism of disease. These data inform diagnostic criteria and management strategies for TTNtv-positive DCM patients and for TTNtv that are identified as incidental findings.

An adult with truncus arteriosus and unilateral pulmonary hypertension.

We report a young man who has persistent truncus arteriosus (TA), severe truncal regurgitation and unilateral pulmonary hypertension. Our patient had palliative main pulmonary artery (PA) banding done during infancy that was not followed by definitive corrective surgery. Unilateral irreversible left sided pulmonary hypertension developed due to migration of the PA band to the right PA. The patient presented to us with infective endocarditis of the truncal valve. This had resolved with medical treatment. Discussion was made on general management of TA and specific difficult management issues of palliated TA in adult, as found in our patient.

Determination of the left ventricular myofiber angle analytically and its significance.

It is known that the tremendous internal pressure build-up in the left ventricle (LV) cavity during isovolumic contraction is due to the contraction of the spirally woven myocardial fibers. In this paper, a biomathematical model is developed to investigate the fiber angle using the theory of elasticity. Simultaneously, another simplified model in order to reduce the mathematical complexity was also developed to determine the fiber angle. The results of these two models showed that both the myocardial fiber angles are in same magnitude.